Tag: Agents

All the articles with the tag "Agents".

• Reproducing CompactRL: What Worked, What Failed, and Why We Did Not Scale

  Updated: 24 Jul, 2026 · 16 min read

  An auditable CompactRL reproduction spanning the public algorithm, a 96-step long-horizon simulation, integration with slime, real Qwen actor-critic training, value-function fixes, 17 experimental phases, and the evidence that stopped us from scaling.

• From Long CoT to Agent Swarms: The Documented Evolution of Kimi's Reinforcement Learning

  18 Jul, 2026 · 15 min read

  A source-grounded history of Kimi's reinforcement-learning stack, from Kimi k1.5's long-context outcome RL and partial rollouts to K2's general RL and K2.5's multimodal GRMs and Parallel-Agent RL.

• Training the Critic Without Crashing the Reward: A Practical Guide to Agentic RL

  12 Jul, 2026 · 20 min read

  A practical framework for critic training and credit assignment in long-horizon LLM agents: IQL, pairwise advantage, hindsight and counterfactual critics, privileged information, turn-level MDPs, chain-of-thought monitoring, and reward-crash diagnosis.

• Scaling RL for White-Collar Work: The Environment Foundry

  3 Jul, 2026 · 20 min read

  A practical framework for turning common white-collar workflows into RL environments: spreadsheets, CRM tasks, customer support, web research, dashboards, and other software-mediated work.

• How to Arbitrarily Increase the Difficulty of Agent Evaluation Sets

  28 May, 2026 · 18 min read

  A practical framework for making agent benchmarks harder in a controlled way: treat difficulty as trajectory-graph complexity, not prompt wording. Covers deterministic scoring, capability facets, harness effects, and systematic data generation.

• Experience-Augmented In-Context Learning: A Training-Free Complement to RL Post-Training

  28 Feb, 2026 · 24 min read

  RL post-training makes models smarter, but it can't cover the infinite long tail of real-world cases. Experience-augmented ICL retrieves successful reasoning traces at inference time, letting agents learn continuously from real usage — no retraining required.